Our results suggest that \(\mathcal {NC}\)  on the source datasets correlates with transferability up to some threshold. However, locating the threshold and explaining the change in transferability above the threshold are challenging under the \(\mathcal {NC}\)  framework. Furthermore, using \(\mathcal {NC}\)  alone to study transfer learning could be off-the-shelf in some circumstances. For example, recent work [1]} showed that representations learned from unsupervised contrastive approaches are uniform over hyperspheres and [2]} illustrated that representations learned using the maximal coding rate reduction (MCR\(^2\) ) principle forming subspaces instead of collapsing to single points. Therefore, further disclosing the mysteries shrouded around transfer learning would require new frameworks and new tools, which we leave for future investigation.
